\(\tilde{H}\)  is a fully connected (or dense) ANN with five layers – one input, one output, and three hidden (HidL), as the one illustrated in fig: ANN architecture. Input and output layers have a number of units (neurons) – input size (InS) and output size (OutS) – coherent with the targeted problem (FxA, FxL, or FxFlA). Each hidden layer has the same hidden size (HidS) of 200 neurons, selected as the optimal one among different settings. ReLU
(Rectified Linear Unit) is the non-linear activation unit (ActUn) for each neuron, and it is defined as \(\phi (x):=\max (x,0)\)  [1]}. The loss function (LossFunc) is the Mean Squared Error (MSE) between the actual outputs, \(\mathbf {a}\)  (available in \(\mathbf {T}\) ), and the ones predicted by the ANN, \(\tilde{H}(\mathbf {p})\) .
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